In current Information Technology (IT) environments, data backup may be a necessary task. The purpose of backup may be to provide protection and operational recoverability on client machines. Generally, a backup application may take snapshots of active data periodically to create backup images in order to provide a method of recovering records that have been deleted or destroyed. Backup operation therefore protects active data that may be changing on a frequent basis. Thus, backup may be designed as a short-term insurance policy to facilitate disaster recovery.
One problem with today's backup applications may be the ability to perform fast searches across all backup data. Since a typical backup operation may involve a large set of data, a vast amount of backup data may be generated because periodic images may be taken in a high frequency (e.g., daily). To search all backup data for specific data items may be a challenge.
One solution may be to use indexing technology, which may use an index engine to index data items to generate one entry for each data item in an index database and may provide fast search capability over large amounts of data. However, one of the problems associated with such a technique may be a limitation of the number of entries generated in the index database by the index engine. For example, one typical index engine has a limit of 6 million entries, and another enterprise index engine has a limit of 30 million entries. In a typical IT environment, the index engine's limit may be reached after a few backup cycles. One way to deal with this problem is to dedicate multiple inter-connected machines for indexing (e.g., federated indexers). However, the problem still remains for using indexing technology without hitting the limit (e.g., without using federated indexers).
In view of the foregoing, it may be understood that there are significant problems and shortcomings associated with current data storage technologies.